Document pre-trained models and grid-based models have proven to be very effective on
various tasks in Document AI. However, for the document layout analysis (DLA) task …

Table structure recognition (TSR) aims at extracting tables in images into machine-
understandable formats. Current approaches address this issue by either predicting the …

Existing fine-grained visual categorization (FGVC) methods assume that the fine-grained
semantics rest in the informative parts of an image. This assumption works well on favorable …

Visual Information Extraction (VIE) plays a crucial role in the comprehension of semi-
structured documents, and several pre-trained models have been developed to enhance …

End-to-end visual information extraction (VIE) aims at integrating the hierarchical subtasks of
VIE, including text spotting, word grouping, and entity labeling, into a unified framework …

Extracting information from scanned images is a critical task with far-reaching practical
implications. Traditional methods often fall short by inadequately leveraging both image and …

Abstract Existing methods for Visual Information Extraction (VIE) from form-like documents
typically fragment the process into separate subtasks, such as key information extraction …

In recent years, the challenge of extracting information from business documents has
emerged as a critical task, finding applications across numerous domains. This effort has …

Visual semantic entity recognition (visual SER) aims to extract contents that fall in key fields
from the given visually-rich document image, and it has been widely applied across diverse …

Document dewarping, aiming to eliminate geometric deformation in photographed
documents to benefit text recognition, has made great progress in recent years but is still far …